This winner of this month’s award for “Unexpected Achievement in the World of Graphs” is Sean Taylor of the Facebook Data Science Team. Rather than describe what has been done, I’ll just leave a link here and say it’s Super Bowl related. It’s better explained by Sean anyway.
We here at GraphGraph appreciate a good graph, but what is getting our spreadsheets all in a pivot right now is dreaming of the amount of data that the good folks* at Facebook have at their disposal.
I have one problem with their presentation, and that would be the use of grey as a color. I understand that with 32 teams, there are only so many color options, and I can’t at this moment say how I would have done it differently. Nevertheless, to my eye, grey always looks like it represents “neutral” or “no data available,” not “Patriots or Colts or maybe even Cowboys.” Oh well.
There is a series of maps that shows the support for each remaining team as this year’s postseason progressed. I immediately wished there was an animated version, so I created a gif for your internetting consumption. Enjoy.
I’d like to see this map redone with the map weighted by population, like they do around election time. I also wouldn’t mind seeing this for other sports, like baseball and basketball and curling. Finally, I would be remiss not to mention two things:
1) Sean, Corey, and I all share the same alma mater.
2) Go Ravens.
*I sincerely hope that they are good, given all of the embarrassing pictures they have of young graph enthusiasts.
Sometimes at work we get requests to visualize data on maps. It’s a really cool feature, but the challenge is generally the calculation being used to drive the chart is a straight sum, and states like California, New York, and Texas always seem to have the highest values.
XKCD had a great comic the other day that I 100% agree with: the problem with geographic heat maps is that it’s essentially just a population map.
This is why anytime you’re putting together a heat map, it’s best to normalize the data as best as you can with a per-capita calculation.
Compare the following two calculations:
Summation -> Sum(Measure)
Per Capita -> Sum(Measure)/Sum(Number of People in that Grouping)
This very simple switch allows you to make a much more effective comparison of large states like California to small states like Rhode Island.
USA Today offers a graph every morning in the bottom-left corner of the front page.
Take a look at this first one:
Why am I not surprised by these results? It’s essentially a “top 5 population” map. The only thing that seems off is that Arizona and Georgia are showing up here, but I’m bringing outside knowledge that the population is not very high there, so I can assume that it must be an outlier.
However a few weeks later I picked up the paper and pleasantly surprised to see this map:
Much better! Now that they’ve switched to a percentage-focused view, I get a much better sense that in these states the proportions are indeed larger when compared to other states.
Map visualizations can be very powerful, but a little simple division can help you get a greater wealth of information from the same pixel space!
During the course of the 2012 U.S. presidential election you’ve no doubt seen lots of maps of the United States.
The maps that most people saw on election night (and in the weeks running up to it) had a very simple binary look: Blue for Obama, and Red for Romney. Usually, they just have one color per state because that’s what matters in the Electoral College.
However, it’s interesting to split that data out into counties as well.
This map (found on Gawker) takes it three steps beyond just the standard red/blue state map. The second map shows counties with a binary red/blue scheme. The third map shows each individual county on a red to purple to blue scale. The final map changes the transparency of any given county based on the population of that county; the brighter the county the more people that live there.
I think this gives a great visualization because it gives a truer perspective of where the votes fell in this election.
Another way to show this data is through a cartogram. Since the presidential election is decided by electoral votes, it makes sense to scale the US appropriately. This cartogram mashes up the two concepts nicely; the shape still resembles the United States, but gives you a more accurate representation in each state’s contribution to the electoral vote total.
The above video was posted on the official Twitter blog and is an interesting use of geospacial visualization over time.
A few things you can gain from the video:
You can see where Twitter has it’s main influence based on where the 1’s are.
The initial wave at 11:11AM in the morning sweeps across the whole globe, but the secondary wave at 11:11PM only really makes a splash in North and Central America. My presumption is because on those continents they use the AM/PM designation vs. other nations that use the 24-hour clock.
The United States has a lot of people in it. However, we wanted to take a look at what was the most populous city within each state, and graph it out.
We took a look only at incorporated cities (as opposed to metropolitan areas) and found some interesting results, especially towards the bottom.
Familiar cities like Boise (ID) and Salt Lake City (UT) are outside the top 100, while cities like Portland (ME) and Cheyenne (WV) aren’t even in the top 500. Burlington, the most populous city in VT, didn’t even crack the top 1000.
Data was compiled from the Census (via Wikipedia 12) for cities with population over 100,000 and the World Gazetteer for the remaining Top 1000 cities.
Geospatial visualization is a fascinating field and we plan on covering it more here on GraphGraph. In my other moonlighting gig, I write for Geekadelphia, my first post was on Geotagging Philadelphia, based on the “Locals and Tourists” project that Eric Fischer put together.
I’m linking to it because I think Eric’s project is a fantastic example of real insight from mashing data with maps. I analyzed Philadelphia because it’s my home, so I encourage you to look at your city and see what information you can learn about where you live.
Can you name the ten most populous cities in North America?
I tried and I know that I didn’t get them all. Specifically, I missed a few cities of our neighbors to the south in Mexico and the north in Canada, which probably speaks to my poor knowledge of geography more than anything else.
Here is the list (taken from Wikipedia), compiled based on the most recent census data from each county:
New York City
Ecatepec de Morelos
It’s nice to have them all in a list, but if we graph this data we can hopefully get some additional value from this information.